Wireless communication devices are incredibly widespread in today's society. For example, people use cellular phones, smart phones, personal digital assistants, laptop computers, pagers, tablet computers, etc. to send and receive data wirelessly from countless locations. Moreover, advancements in wireless communication technology have greatly increased the versatility of today's wireless communication devices, enabling users to perform a wide range of tasks from a single, portable device that conventionally required either multiple devices or larger, non-portable equipment.
Smartphones and other mobile devices can contain sensors. These sensors may include, but are not limited to, motion sensors such as accelerometers, gyroscopes, etc., and environment sensors such as thermometers, light sensors, microphones, etc. The output of a device's motion sensors is indicative of the movements of the device. The device movements contain information about both the motion state of the user (e.g. sitting, standing, walking, running, etc. . . . ) and the device position with respect to the user (e.g. pocket, backpack, hand, desk, etc. . . . ).
States corresponding to particular sensor outputs can be learned, so that sensor data can subsequently be used to determine unknown device states. For example, during a training procedure, a device configured to execute a classification algorithm (e.g., a Bayesian classifier, etc.) may be exposed to examples of motion state/device position combinations, and may process corresponding sensor data to learn a model for each combination. Then, when presented with a new set of sensor information for an unknown motion state/device position, the classifier will select both the motion state and the device position that have the highest computed likelihoods (or posteriors, if prior probabilities are known).
Such classification algorithms may operate based on identified features and given statistical models. For example, a Gaussian Mixture Model (GMM) with 16 mixture components may be utilized to estimate motion state. As another example, a GMM with 2 mixture components may be utilized to estimate device position. Techniques that provide further improvement of device state classification are desirable.